The principal and immediate objective for the proposed research is to determine empirically, using accurate models of the human breast, the most effective methods of training for lump detection. Physical tests will be performed on human subjects to determine the range of size, firmness, and nodularity of human breasts and a series of models constructed to duplicate these ranges. Simulated tumors will then be incorporated into these models. Concurrently, a system for automatically analyzing search pattern activity will be perfected in which a small computer quantifies a trainee's search pattern and provides her with feedback on the effectiveness of her search. The feedback technology is expected to reinforce detection of the smallest palpable lumps by delivering immediate visual and auditory confirmation of successful detection. Studies are described which will isolate the effects on lump detection of breast size, firmness and granularity. Also, detection differences due to the location, consistency, shape, and fixation of lumps will be carefully analyzed. The effects on detection of personal variables including searching pattern, prior training, pressure exerted and retention of the skill will be systematically evaluated. A new home Breast Self-Examination program (BSE) including several prompting agents will be investigated and the effective components compiled in kit form. The major significance of this project is the potential reduction of morbidity and mortality due to breast cancer through increases in the accuracy and regularity of BSE and decreases in size of tumors detected.